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Distributed Computing World Climate Simulation
Burnt Offerings writes: "The BBC reports that scientists at climateprediction.com are nearing the completion and public release in late summer of a distributed computing project that simulates the world's climate from 1950-2050 AD. It seems that each user's simulation will have different initial conditions built into their runtime simulation and a single completed simulation from 1950-2050 AD takes on average eight-months (Doh!), assuming average household computing power. The results will be sent back to the project's team, where they will select the models that resulted in the 'real' climate patterns that have occured since 1950-2000. I presume they will then use these validated models to help extrapolate the world's climate from 2000-2050. Pretty cool (or should I say warm? or hot?)."
The end result of the project:
"On 1st January, 2050, it will start rather cloudy with outbreaks of rain, mainly in the north. These will clear up by late afternoon, leaving it warm with mild breezes in most of the country."
graspee
I bet I can take just 48 years without even using a PC and have an ever more correct answer then everyone using the lastest pc.
Blame the climate changes from 1950 to 2000 on the expanded use of the automobile and unregular industrial waste. Do you think any scientist in 1950 could have known about our current situation? How can we in 2000 know about the new problems that'll creep up between now and 2050?
Spend your extra CPU cycles computing the cure for cancer or finding ET. I doubt this will prove useful.
The information on their website says the time step is 30 minutes and that their box is 3.75 degrees longitude by 2.25 degrees latitude (or visa versa: BIG, in any event).
Therefore, how do they expect this to work -- additionally absent any outside changes in the environment?
What I mean is, how do they know if they did a good job? Perhaps if the results are all very close to the current day climate, I'd buy that they got it right, but if they have a reasonable distribution of results, how do you decide? I mean, we've been clear-cutting the hell out of forests left and right for years: do they somehow takes this into account? Heck, how do they present the geographic information about the Earth: this bit has forest, this bit is desert. I would think that this would make quite a bit of difference in results (changes in albedo, for instance).
I certainly wish them luck, but they're not getting my PC for that long without something more detailed , informationwise.
Weather is chaotic, but climate is ... well, ok, climate might be chaotic, but we really don't know -- and if it is chaotic, it is still only chaotic on timescales of more than 50 years.
Predicting climate 50 years in the future is a computationally difficult task, but it isn't impossible the way that predicting weather would be.
Tarsnap: Online backups for the truly paranoid
Global warming accelerated by CPU heat as weather enthusiasts simulate climate with computer. Temperature for the next 2 years will rise by 2 degrees
In simulation A we set the Funding Amount variable to 0$ and the Donating Corporation to NULL. Their results was intense global warming in 2050.
In simulation B we set the Funding Amount variable to 200,000$ and the Donating Corporation to Exxon Mobile. Their result was no global warming at all in 2050.
In simulation C we set the Funding Amount variable to 300,000$ and the Donating Corporation to Amazon Lumber Harvesters. Their result was an actual decrease in green house gases by the year 2050 due to deforestation.
In simulation D...
Outdoor digital photography, mostly in New Engl
What is climate but (basically dumbing it down) taking the average of the last x number of years of weather to define the norm. So, to define what the climate is fifty years into the future, one would have to take a look at the weather for each of those years. I agree that is no small task.
I must take issue with the parent post, though. I agree that weather is a choatic system, very much so. But, all aspects of weather can be parameterized, even the most chaotic ones. The key here is a matter of scale. The mesoscale type systems are extremely hard to model, but you take a global system (long wave patterns), and you will have a much better time of modeling them. How? You throw out the small scale stuff like your butterfly and such. On a global scale, something like that would quickly disappear into the larger scale. That is why global models (like the MRF, NOGAPS, and such) work better out farther (those models run out to 384 hours as opposed to smaller scale models that run out 84). Verification rates are acceptable for those models out that far (numbers I cannot quote off the top of my head). They could do better, but they would require more time to process and would not be useful to the operational meteorologist.
This distributed system will be over eight months and on such a large scale, the results will be useful.
Bryan R.
The price of freedom is eternal vigilance, or $12.50 as seen on eBay.....
It's generally regarded as a Bayesian technique. Actually, there's far more to Bayesian statistics that bootstrapping, but it's the part I spend a lot of time working with. In fact, I suppose that bootstrapping isn't fundamentally a Bayesian process, but it is highly empirical so it appeals to the same "crowd" as more decidedly Bayesian techniques. By the by, "Bayesian" statistics are statistics that make heavy use of Bayes' Rule to incorporate prior knowledge not included in your measured data.
My background - you develop a program to predict something biological. Let us say, to pick a problem on the same order of difficulty as predicting the weather, that you're trying to predict the three dimensional confirmation that proteins assume, based on their sequence.
Now, okay, you have a bunch of known sequences, which other people (personally, I do both the data mining and some crystalography) have attached to known structures. So, what do you do?
Well, you could fiddle with your program until it predicts really well on those sequences, and announce that it was good. This is "Bad Science", as the parent-poster points out, since the criterion are arbitrary - you have a tendency to "discover" random noise in the data, and you have no way of validating your results.
So, second option. Instead, you split the data in half at random (actually into more than 2 pieces, but conceptually in half.) You take one half, and you make the model predict as well as you can on that data. Then, you VALIDATE ON THE OTHER HALF OF THE DATA. You *never* change the model on the basis of the second half of the data - that is arbitrary/bad/cheating. This is called "bootstrapping". It has nothing to do with compiler installation.
So, as far as most scientists (as opposed to mathematicians) are concerned, the important question is - does this work? In the biological sciences, I can say categorically, yes, this bootstrapping technique has a proven track record. It does work. Obviously, you can screw up (using non-representative data is a good start) but the technique, when properly applied, is sound.
So, I assume it would work for predicting the weather, as well. By work I mean - you would know how well your software predicted the weather. Bootstrapping is not a means of predicting the weather in and of itself, merely of honestly evaluating the effectiveness of a weather prediction mechanism you already have.
The good and new comes from no quarter where it is looked for, and is always something different from what is expected.
They're starting with different initial conditions and hoping that some subset results in 50 years of weather?
Shouldn't they use the last 50 years of weather as initial conditions and vary parameters of the model instead?
What they're doing is like flipping an imaginary coin 500 times hoping to match the first 250 flips of a real coin to predict the the last 250 flips (albeit in a system with non-independent trials). But then they're taking those 500 flips and matching the first 250 to weather reports (might as well be coin flips) and then imagining the next 250 flips will approximate the future weather reports. What they need to do is fix the initial conditions and modify the model (coin flips vs. rolls of the die vs. LCRNG, etc.) to find a model that approximates the dynamics of the system.
Am I making sense here? How are these bozos not just going to apply their effective innumeracy to waste a few trillion CPU hours that could otherwise have been used to do protein folding or cancer-killing molecule matching?
--Blair
Also these people are entirely too green and liberal for my tastes. At first it is a very thought provoking idea. But these people already have preconcevied conclusions... and that isn't very good science.
On the contrary, scientists first formulate a hypothesis (in other words, a preconceived notion; human activity has led to global warming, for instance) and then perform an experiment to test it. And like it or not, global warming is occurring. The average temperature of the planet is rising, which is all that is meant by global warming. Whether or not this is the result of human action is still being contested. <OPINION>But personally, I would be very shocked if human activity has had NO effect whatsoever on the climate of the planet.</OPINION>
As one poster has pointed out, weather is a chaotic system (and climate is also chaotic by definition).
Chaos is gravely misunderstood though so let me real quick through in my explaination for why this experiment will just generate FUD.
Chaotic equations are chaotic not because of the number of variables involved but because of the interdependency on themselves (each iteration requires the former iteration). This leads to extreme sensitive dependency on initial conditions (a.k.a. the Butterfly Effect). I should have probably emphasized the word extreme because even the slightly deviation will produce dramatically different results.
Even the best climate prediction algorithm would be crap if the initial condition was off by 10^(-20). The fact that we cannot measure temperatures exactly means that we could never feed a perfect initial condition.
Chaotic equations do have a given period before divergence gets extreme when initial conditions are altered. The original equations that Lorenz used (the pioneer of weather forecasting and the father of Chaos theory) showed divergence after about three days (which is why five-day forecasts still suck to this day).
I find it very hard to believe that these folks have developed an equation that doesn't show divergence for 100 years. Not to mention the fact that the number of initial conditions are much larger than the project makes them out to be.
Summary: Some PhD is looking for research money and figures that mixing "scientific" proof for global warming, chaos, and SETI-style distributed computer has to be good for a couple million at least.
int func(int a);
func((b += 3, b));
... or at least the best science has come up with so far, are downloadable from the Intergovernmental Panel on Climate Change (IPCC).
I'd start with the Summaries for Policy Makers, as a way of becoming very well infomrmed in just ~20pp.
AFAIK: It's a UN organization that is the center of research. Their reports are a consensus of almost all the leading scientists from every country on the globe, and their policy statements are approved line-by-line by governments. Even with all that, there are pretty strong statements.Here's better background.
Yes. In fact, any system which displays locally nonlinear disturbances in a globally linear function will do so.
The mere fact that climate is study of average weather is irrelevant to the system at hand.
No it isn't. It should immediately alert you to the possibility that climate might be more predictable than weather. Averages always have lower variance than the underlying data.
A chaotic system will by definition exhibit divergence either way with a slight change in initial conditions
This isn't a rigourous definition you're talking about here, and your definition doesn't prove your point. A chaotic system might exhibit divergent behaviour, but that doesn't necessarily require that the divergence be either permanent of large in relation to an underlying linear trend. For example, if I take the output of a nonlinear oscillator and add it to the signal for Radio Luxembourg, I can make a system which is "chaotic" in the sense that its local behaviour is divergent in a nonlinear way dependent on small variations in initial conditions. But I can still extract a useful signal from my system by applying the right filter.
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